Oh so some random user with no credentials, reputation, or real name just typed "do deep research on the AI infrastructure financial bubble and write a report" then submitted it to HN?
Why should I bother reading what may or may not be a pile of unverified hallucinations?
No, but close. Someone like that built the infrastructure tooling to do deep research, wrote up their process doing that, and then did what you said after, which I consider to be different.
I didn’t read it in full but I spot checked one or two citations and I found them compelling.
Appreciate the anecdote and your other comments on HN. But I strongly suspect you are incredibly atypical based on your background and previous work experience in ways that would tremendously down weight the probability that any part of your experience with recruiters would apply to even above average engineers.
Anecdote: As a hapless junior engineer I once did something extremely similar.
I ran a declarative coding tool on a resource that I thought would be a PATCH but ended up being a PUT and it resulted in a very similar outcome to the one in this post.
Yeah that's the typical junior engineer scenario right? Run a command that wasn't meant to be destructive but accidentally destroy something. This is different. AI agent went on some kind of wild goose chase of fixing problems, and eventually the most probable token sequence ended up at "delete this database". This is more like if your senior engineer with extreme ADHD ate a bunch of acid before sitting down to work.
I would argue that “Why did you do that?” between humans is usually a social thing not a literal request for information.
What the asker wants is evidence that you share their model of what matters, they are looking for reassurance.
I find myself tempted to do the same thing with LLMs in situations like this even though I know logically that it’s pointless, I still feel an urge to try and rebuild trust with a machine.
The only correct way to ask an AI "why did you do that?" is in the sense of a blameless postmortem. You're the person responsible for giving the LLM appropriate context and instructions and guardrails, so the only reason you should ever ask a question like that is when you're genuinely trying to figure out how to improve those for next time. Every time I see people posting this sort of "apology" from an LLM it makes me cringe, feels only half a step away from outright AI psychosis.
I’m not sure what you mean? I have goals that I want to achieve; lil ai buddy comes along and helps me, over time buddy becomes better able to help me do stuff.
What do you mean role? Person who does stuff I guess, same as it is now.
Because I'm the one employing it? A model which makes a "delete production database" mistake clearly needs to be taught not to do that, and the person whose production database was deleted ought to be able to teach them not to do that. This seems quite reasonable to me.
Everyone has different mistakes that are important to them, and not everyone agrees on what is a mistake. Witness how employees from FAANG often don't do well at startups, because they've been taught the wrong lessons.
There is something darkly comical about using an LLM to write up your “a coding agent deleted our production database” Twitter post.
On another note, I consider users asking a coding agent “why did you do that” to be illustrating a misunderstanding in the users mind about how the agent works. It doesn’t decide to do something and then do it, it just outputs text. Then again, anthropic has made so many changes that make it harder to see the context and thinking steps, maybe this is an attempt at clawing back that visibility.
If you ask humans to explain why we did something, Sperry's split brain experiment gives reason to think you can't trust our accounts of why we did something either (his experiments showed the brain making up justifications for decisions it never made)
Bit it can still be useful, as long as you interpret it as "which stimuli most likely triggered the behaviour?" You can't trust it uncritically, but models do sometimes pinpoint useful things about how they were prompted.
The purpose of firing a person shouldn't be vengeance but to remove someone who is unreliable or not cost effective.
It's similarly reasonable to drop a tool that's unreliable, though I don't think that's a reasonable description here. Instead, they used a tool which is generally known to be unpredictable and failed to sandbox it adequately.
The purpose of firing a person is to remove someone unreliable, but also, the person having that skin in the game makes him behave more reliably. The latter is something you cannot do with an LLM.
The cold hard fact is: LLMs are an unreliable tool, and using them without checking their every action is extremely foolish.
"The cold hard fact is: LLMs are an unreliable tool, and using them without checking their every action is extremely foolish."
You mean checking every action of theirs outside the sandbox I suppose? Otherwise any attempt at letting an agent do some work I would consider foolish.
I disagree. They could fire Claude and their legal counsel could pursue claims (if there were any, idk)-- the accountability model is similar. Anthropic probably promised no particular outcome, but then what employee does?
And in the reverse, if a person makes a series of impulsive, damaging decisions, they probably will not be able to accurately explain why they did it, because neither the brain nor physiology are tuned to permit it.
Do we? Or are we born with pre-training (all the crucial functions the brain does without us having to learn them) and a context window orders of magnitude larger than an LLM?
It is incredible how willing and eager AI boosters are to denigrate the incredible miracle of human consciousness to make their chatbots seem so special.
No, we are not born with all the pre-training we need. That is rather the point of education, teaching people's brains how to process information in new, maybe unintuitive ways.
Yup. And eventually there will be online learning, that doesn't require a formal update step. People keep conflating the current implementation, as an inherent feature.
What does that actually mean in practice? You can yell at human if it makes you feel better, sure, but you can do that with an AI agent too, and it's approximately as productive.
You might as well be asking a tape recorder why it said something. Why are we confusing the situation with non-nonsensical comparisons?
There is no internal monologue with which to have introspection (beyond what the AI companies choose to hide as a matter of UX or what have you). There is no "I was feeling upset when I said/did that" unless it's in the context.
There is no ghost in the machine that we cannot see before asking.
Even if a model is able to come up with a narrative, it's simply that. Looking at the log and telling you a story.
Sperry's experiments makes it quite clear that the comparison is not nonsensical: humans can't reliably tell why we do things either. It is not imbuing AI with anything more to recognise that. Rather pointing out that when we seek to imply the gap is so huge we often overestimate our own abilities.
Humans at least have a mental state that only they are privy to to work from, and not just their words and actions. The LLM literally cannot possibly have a deeper insight into the root cause than the user, because it can only work from the information that the user has access to.
> Humans at least have a mental state that only they are privy to to work from
Maybe. How do you tell? What would you expect to be different if they didn't?
> The LLM literally cannot possibly have a deeper insight into the root cause than the user, because it can only work from the information that the user has access to.
Insight is not solely a function of available input information. Arguably being able to search and extract the relevant parts is a far more important part of having insights.
>Maybe. How do you tell? What would you expect to be different if they didn't?
I think you're asking how I would know if other people were P-zombies. That's an inappropriate question because I didn't talk about subjective experience, just about internal state. There's no question about whether other people have internal states. I can show someone a piece of information in such a way that only they see it and then ask them to prove that they know it such that I can be certain to an arbitrarily high degree that their report is correct.
Unvoiced thoughts are trickier to prove, but quite often they leave their mark in the person's voiced thoughts.
>Insight is not solely a function of available input information. Arguably being able to search and extract the relevant parts is a far more important part of having insights.
LLMs are notoriously bad at judging relevance. I've noticed quite often if you ask a somewhat vague question they try to cold-read you by throwing various guesses to see which one you latch onto. They're very bad at interpreting novel metaphors, for example.
> I didn't talk about subjective experience, just about internal state. There's no question about whether other people have internal states. I can show someone a piece of information in such a way that only they see it and then ask them to prove that they know it such that I can be certain to an arbitrarily high degree that their report is correct.
Well, sure, but that much is equally true for an LLM with a scratchpad or what have you. (I guess you could say that the user should have access to the LLM's scratchpad and therefore be just as able to understand the state as the LLM itself, but as we move towards the LLM using its own state vectors that's less and less true in practice). I agree that a human may have a mood or secret knowledge or what have you in a way that an LLM wouldn't, but if all you're positing is access to some inert but hidden state then that feels like a Toaster-Enhanced Turing Machine.
>if all you're positing is access to some inert but hidden state then that feels like a Toaster-Enhanced Turing Machine
I thought it was pretty clear, given the context. What I'm saying is that humans are capable of limited introspection in ways that LLMs are not. They can remember their thought processes and review them ex post facto to answer questions that LLMs cannot. An LLM fundamentally cannot truthfully answer questions such as "why did you do this?" because its entire working memory is held in the context window. It doesn't know to any greater degree than you because it has no more information than you do; just like they are for you, its internal workings are a mystery. I'm not saying LLMs conceptually could not be designed with capabilities similar to a human's in this regard, with some symbolic memory that's capable of some bookkeeping, I'm saying none of the current ones have them.
> I didn't talk about subjective experience, just about internal state. There's no question about whether other people have internal states. I can show someone a piece of information in such a way that only they see it and then ask them to prove that they know it such that I can be certain to an arbitrarily high degree that their report is correct.
> What I'm saying is that humans are capable of limited introspection in ways that LLMs are not. They can remember their thought processes and review them ex post facto to answer questions that LLMs cannot.
But now you're making a much stronger claim than merely saying that internal state exists. Humans are capable of telling you a story about what their thought process was (as are LLMs). But whether that story will be accurate, much less contain new insights, is much harder prove.
>But now you're making a much stronger claim than merely saying that internal state exists.
It's not a different claim, it's the same claim. The reason humans are able to introspect is because they have that internal state.
>Humans are capable of telling you a story about what their thought process was (as are LLMs)
No. Humans can tell a story that's informed by introspection, while LLMs can only tell a story without any introspection. Humans may also lie and fabricate, but they are at least capable of introspecting, while LLMs are not.
>But whether that story will be accurate, much less contain new insights, is much harder prove.
If you're going to doubt the explanation then what's the point of asking the question? Necessarily it's going to be information that exists only in that person's mind, so at best you can check it for consistency with the person's own behavior and with the report itself, but some things you'll just have to either accept or ignore. Like, fundamentally you're asking the person to describe features of their own mind such as "he gets bored easily", "he can only hold so many facts at once", "he makes worse decisions under pressure", etc. If for example you're asking the question to improve something in the future (such as documentation or some procedure), it doesn't even make sense to distrust such reports, unless you believe a person like the one being described by the explanation doesn't and can't exist.
> It's not a different claim, it's the same claim. The reason humans are able to introspect is because they have that internal state.
> No. Humans can tell a story that's informed by introspection, while LLMs can only tell a story without any introspection. Humans may also lie and fabricate, but they are at least capable of introspecting, while LLMs are not.
There's still a gap here between "has some hidden internal state" and "that state can provide insight into to their thought process". If all you've shown is that knowledge that is public in LLMs is hidden in humans, there's no reason that should make the human better at introspecting (rather, it just makes the human harder to understand from outside).
> what's the point of asking the question?... If for example you're asking the question to improve something in the future (such as documentation or some procedure)
Indeed. If we knew that asking this kind of question of a human was more likely to provide insights that improved the process in the future than asking it of an LLM, that would be interesting. But it's quite a leap from "humans can have internal state" to that.
> unless you believe a person like the one being described by the explanation doesn't and can't exist
Meaning that a plausible explanation is valuable regardless of whether it's true? Wouldn't that apply just as well to an LLM's explanation?
>There's still a gap here between "has some hidden internal state" and "that state can provide insight into to their thought process".
No, because that internal state is part of the thought process. That's the whole point. You ask the human a question to learn something that you don't already know. It makes no sense to ask an LLM that because it knows nothing you don't already know; you and the LLM are privy to the exact same information. What's tripping you up about this?
>If we knew that asking this kind of question of a human was more likely to provide insights that improved the process in the future than asking it of an LLM, that would be interesting.
So, at this point I must ask: are you an NPC? Do you go through life just reacting to stimuli like a cockroach, with no understanding of why or how you do anything? If you're playing chess and someone asks you about a move you just made you are unable to explain, "I noticed such-and-such so I decided the best course of action was so-and-so to prevent this-and-that"? This is an alien concept to you? If so, then I'm sorry; most of us do not experience our own cognition in this way. We can perceive the formation of our own thoughts as well as the progressive retrieval of information.
>Meaning that a plausible explanation is valuable regardless of whether it's true? Wouldn't that apply just as well to an LLM's explanation?
> There's no question about whether other people have internal states. I can show someone a piece of information in such a way that only they see it and then ask them to prove that they know it such that I can be certain to an arbitrarily high degree that their report is correct.
> No, because that internal state is part of the thought process. That's the whole point. You ask the human a question to learn something that you don't already know.
If the internal state is entangled enough with in the thought process that it would help with providing insights, sure. But I don't know that humans have such state accessible to them, and the fact that humans can know facts that are not accessible from outside does not in itself convince me of that.
> It makes no sense to ask an LLM that because it knows nothing you don't already know; you and the LLM are privy to the exact same information.
OK but why does that mean that the LLM's explanation should be bad/useless, if the only difference is that I have more direct access to the LLM's information than I would to a human's information?
> So, at this point I must ask: are you an NPC? Do you go through life just reacting to stimuli like a cockroach, with no understanding of why or how you do anything? If you're playing chess and someone asks you about a move you just made you are unable to explain, "I noticed such-and-such so I decided the best course of action was so-and-so to prevent this-and-that"?
I can tell stories about my own cognition. Those stories feel real to me. But I'm aware that the best available scientific evidence suggests that they're indistinguishable from confabulations.
It is non-sensical because you're simply bringing in comparisons without anything linking the two. You might as well be talking about how oranges, and bicycles think as well as that is just as relevant as how humans think in this discussion.
In fact, talking about "thinking" at all is already the wrong direction to go down when trying to triage an incident like this. "Do not anthropomorphize the lawnmower" applies to AI as much as Larry Ellison.
I think you might be misinterpreting that. I always understood it to mean that when the two hemispheres can't communicate, they'll make things up about their unknowable motivations to basically keep consciousness in a sane state (avoiding a kernel panic?). I don't think it's clear that this happens when both hemispheres are able to communicate properly. At least, I don't think you can imply that this special case is applicable all the time.
We have no reason to believe it is a special case. The fact that these patients largely functioned normally when you did not create a situation preventing the hemispheres from synchronising suggests otherwise to me. There's no reason to think the ability to just make things up and treat it as if it is truthful recollection would just disappear because there are two halves that can lie instead of just one.
The thing is, the LLM mostly just states what it did, and doesn't really explain it (other than "I didn't understand what I was doing before doing it. I didn't read Railway's docs on volume behavior across environments."). Humans are able of more introspection, and usually have more awareness of what leads them to do (or fail to do) things.
LLMs are lacking layers of awareness that humans have. I wonder if achieving comparable awareness in LLMs would require significantly more compute, and/or would significantly slow them down.
I agree that the model can help troubleshoot and debug itself.
I argue that the model has no access to its thoughts at the time.
Split brain experiments notwithstanding I believe that I can remember what my faulty assumptions were when I did something.
If you ask a model “why did you do that” it is literally not the same “brain instance” anymore and it can only create reasons retroactively based on whatever context it recorded (chain of thought for example).
> In fact, most of the time models fail to demonstrate introspection—they’re either unaware of their internal states or unable to report on them coherently.
There is no way for a user to know whether the LLM has introspection in a given case or not, and given that the answer is almost always no it is much better for everyone to assume that they do not have introspection.
You cannot trust that the model has introspection so for all intents and purposes for the end user it doesn't.
Claude code and codex both hide the Chain of Thought (CoT) but it's just words inside a set of <thinking> tags </thinking> and the agent within the same session has access to that plaintext.
Those are just words inside arbitrary tags, they aren't actually thoughts. Think of it as asking the model to role play a human narrating his internal thought process. The exercise improves performance and can aid in human understanding of the final output but it isn't real.
Why do you believe that humans have access to an “internal thought process”? I.e. what do you think is different about an agent’s narration of a thought process vs. a human’s?
I suspect you’re making assumptions that don’t hold up to scrutiny.
I made no such claim and I don't understand what direct relevance you believe the human thought process has to the issue at hand.
You appear to be defaulting to the assumption that LLMs and humans have comparable thought processes. I don't think it's on me to provide evidence to the contrary but rather on you to provide evidence for such a seemingly extraordinary position.
For an example of a difference, consider that inserting arbitrary placeholder tokens into the output stream improves the quality of the final result. I don't know about you but if I simply repeat "banana banana banana" to myself my output quality doesn't magically increase.
> I don't understand what direct relevance you believe the human thought process has to the issue at hand.
You're the one who raised it. Perhaps you should clarify what you mean by "isn't real" - do you believe a human narrating their thought process is saying something that's more real?
Someone else replied to your comment asking essentially the same question, perhaps better phrased:
> What would be different if it was "real"? What makes you think that when humans "narrate" "their" "internal thought process", it's any more "real"?
No, I did not raise it. I said that X is false. You responded with "why do you think Y is true" and now you ask "do you believe that Y is true" neither of which is relevant to X being true or false. Humans and LLMs are not the same thing. The colloquial term for this is whataboutism.
What do I mean by isn't real? Exactly what I said originally. It's a roleplay of something that sounds plausible as opposed to what actually happened. There is obviously some process that is producing the output. The thinking trace is not a representation of that underlying process. Rather the thinking trace is an adjacent output of that same process.
Given that LLMs can speak basically any language and answer almost any arbitrary question much like a human would, the claim that LLMs have comparable (not identical) thought processes to humans does not seem extraordinary at all.
What does that mean though, to “have access to our underlying thoughts”? Humans can obviously mentally do things that are impossible for a language model to do, because it’s trivial to show that humans do not need language to do mental tasks, and this includes things related to thought, so I don’t really get what is being argued in the first place.
> it’s trivial to show that humans do not need language to do mental tasks
LLMs don't need language to do mental tasks, either. Their input and output is language - like humans - but in between, the high-dimensional vector representations (often loosely called latent space) are not language in any meaningful sense.
LLMs can benefit from "thinking out loud" much as humans can. The issue is not whether the supposed "thoughts" are actually representative on any "internal" thoughts, but rather that explicating the problem in more detail can help reach better conclusions.
One point I was making is that the idea that humans are doing something "special" (or in the OP comment's terms, "real") in this area isn't well-supported, in fact there's plenty of evidence against it.
> LLMs can benefit from "thinking out loud" much as humans can.
The two processes aren't equivalent. An LLM that fills the thinking trace with a meaningless placeholder token will still exhibit improved performance. There are also regularly things in the thinking trace that don't match the final output if you look closely but on the surface they appear convincing.
It's largely a trained performance. If you go in with the erroneous expectation that it accurately reflects the underlying thought process then you're likely to come away with faulty conclusions.
My point is that language is not a requirement for humans to perform mental tasks absolutely. It is a fundamental requirement of a large language model.
That's a meaningless argument of definition. Replace the language input and output with something else and it's no longer termed an LLM. It's like saying that a "human who writes with right hand" fundamentally requires his right hand in order to write anything because without it he is no longer a "human who writes with right hand" despite that he is still writing (now with his left hand).
I ask a human "predict what a mouse would do here". In an effort to understand why the prediction is sometimes wrong I ask "walk me through what the imaginary mouse is thinking". Upon examination I exclaim "aha! there's the error" but sadly it's not actually because the output prediction was not based on the thinking trace in any robust manner.
That's a loose analogy but it fails to fully illustrate the degree of decoupling here. For example the weirdness of LLM performance being increased via the output of empty sequences.
> I ask a human "predict what a mouse would do here". In an effort to understand why the prediction is sometimes wrong I ask "walk me through what the imaginary mouse is thinking". Upon examination I exclaim "aha! there's the error" but sadly it's not actually because the output prediction was not based on the thinking trace in any robust manner.
Is this meant to be an analogy for a human or an LLM? Where would it be different in the other case?
It does have access to its thoughts. This is literally what thinking models do. They write out thoughts to a scratch pad (which you can see!) and use that as part of the prompt.
It's important to be aware that while those "thoughts" can be a useful aid for human understanding they don't seem to reliably reflect what's going on under the hood. There are various academic papers on the matter or you can closely inspect the traces of a more logically oriented question for yourself and spot impossible inconsistencies.
It doesn’t mean that these “thoughts” influenced their final decision the way they would in humans. An LLM will tell you a lot of things it “considered” and its final output might still be completely independent of that.
That is absolutely not what the split brain experiment reveals. Why would you take results received from observing the behavior of a highly damaged brain, and use them to predict the behavior of a healthy brain? Stop spreading misinformation.
Such 'highly damaged' brain is still 90 percent or more structured the same as a normal human brain. See it as a brain that runs in debug mode.
It is known that the narrative part of the brain is separate from the decision taking brain. If someone asks you, in a very convincing, persuasive way, why you did something a year ago and you can't clearly remember you did, it can happen that you become positive that you did so anyway. And then the mind just hallucinates a reason. That's a trait of brains.
> If someone asks you, in a very convincing, persuasive way, why you did something a year ago and you can't clearly remember you did, it can happen that you become positive that you did so anyway. And then the mind just hallucinates a reason. That's a trait of brains.
Yes brains can hallucinate reasons, doesn't mean they always do. If all reasons given were hallucinations then introspection would be impossible, but clearly introspection do help people.
> a misunderstanding in the users mind about how the agent work
On top of that the agent is just doing what the LLM says to do, but somehow Opus is not brought up except as a parenthetical in this post. Sure, Cursor markets safety when they can't provide it but the model was the one that issued the tool call. If people like this think that their data will be safe if they just use the right agent with access to the same things they're in for a rude awakening.
From the article, apparently an instruction:
> "NEVER FUCKING GUESS!"
Guessing is literally the entire point, just guess tokens in sequence and something resembling coherent thought comes out.
> systemic failures across two heavily-marketed vendors that made this not only possible but inevitable.
> No confirmation step. No "type DELETE to confirm." No "this volume contains production data, are you sure?" No environment scoping. Nothing.
> The agent that made this call was Cursor running Anthropic's Claude Opus 4.6 — the flagship model. The most capable model in the industry. The most expensive tier. Not Composer, not Cursor's small/fast variant, not a cost-optimized auto-routed model. The flagship.
Naw, we just want people to know. We followed all Cursor rules, thought we had protected all API keys, and trusted the backups of a heavily used infrastructure company. Cautionary tale sharing with others.
It’s a good cautionary tale -- in hindsight the danger signs are clear, but it’s also clear why you thought it was OK and how third parties unfortunately let you down.
The “agent’s confession” is the least interesting and useful part of the whole saga. Nothing there helps to explain why the disaster happened or what kind of prompting might help avoid it.
The key mistake is accidentally giving the agent the API key, and the key letdown is the lack of capability scoping or backups in the service.
The main lessons I take are “don’t give LLMs the keys to prod” and “keep backups”. Oh, and “even if you think your setup is safe, double-check it!”
Yes, you're right, in that there's no decision module separate from the output. It overcommits in the other direction.
The post-hoc reasoning the model produces when you ask "why did you do that" is also just text, and yet that text often matches independent third-party analysis of the same behavior at well above chance. If it really were uncorrelated text-completion, the post-hoc explanation should not align with the actual causes more than randomly. It does, frequently enough that I've stopped using it as evidence the user is naive.
"just outputs text" is doing more work than we acknowledge. The person asking the agent "why did you do that" might be an idiot for expecting anything more than a post-hoc rationalization, but that's exactly what you'd expect from a human too.
While I largely agree, it does raise the prospect of testing this iteratively. E.g., give a model some fake environment, prompt it random things until it does something "bad" in your fake environment, and then fix whatever it claims led to its taking that action.
If you can do this and reliably reduce the rate at which it does bad things, then you could reasonably claim that it is aware of meaningful introspection.
Beyond that, isn't it just going to make up a narrative to fit what's in the prompt and context?
I don't think there's any special introspection that can be done even from a mechanical sense, is there? That is to say, asking any other model or a human to read what was done and explain why would give you just an accounting that is just as fictional.
Not necessarily. The people saying that in this thread seem to be forgetting about the encrypted reasoning tokens. The why of a decision is often recorded in a part of the context window you can't see with modern models. If you ask a model, "why did you do that" it isn't necessarily going to make up a plausible answer - it can see the reasoning traces that led up to that decision and just summarize them.
> asking a coding agent “why did you do that” to be illustrating a misunderstanding in the users mind about how the agent works
I think the same thing, but about agents in general. I am not saying that we humans are automata, but most of the time explanation diverges profoundly from motivation, since motivation is what generated our actions, while explanation is the process of observing our actions and giving ourselves, and others around us, plausible mechanics for what generated them.
> It doesn’t decide to do something and then do it, it just outputs text.
We can debate philosophy and theory of mind (I’d rather not) but any reasonable coding agent totally DOES consider what it’s going to do before acting. Reasoning. Chain of thought. You can hide behind “it’s just autoregressively predicting the next token, not thinking” and pretend none of the intuition we have for human behavior apply to LLMs, but it’s self-limiting to do so. Many many of their behaviors mimic human behavior and the same mechanisms for controlling this kind of decision making apply to both humans and AI.
When a human asks another human “why did you do X?”, the other human can of course attempt to recall the literal thoughts they had while they did X (which I would agree with you are quite analogous to the LLMs chain of thought).
But they can do something beyond that, which is to reason about why they may have the beliefs that they had.
“Why did you run that command?”
“Because I thought that the API key did not have access to the production system.”
When a human responds with this they are introspecting their own mind and trying to project into words the difference in understanding they had before and after.
Whereas for an agent it will happily include details that are not literally in its chain of thought as justifications for its decisions.
In this case, I would argue that it’s not actually doing the same thing humans do, it is creating a new plausible reason why an agent might do the thing that it itself did, but it no longer has access to its own internal “thought state” beyond what was recorded in the chain of thought.
> Whereas for an agent it will happily include details that are not literally in its chain of thought as justifications for its decisions.
Humans do this too, ALL THE TIME. We rationalize decisions after we make them, and truly believe that is why we made the decision. We do it for all sorts of reasons, from protecting our ego to simply needing to fill in gaps in our memory.
Honestly, I feel like asking an AI it’s train of thought for a decision is slightly more useful than asking a human (although not much more useful), since an LLM has a better ability to recreate a decision process than a human does (an LLM can choose to perfectly forget new information to recreate a previous decision).
Of course, I don’t think it is super useful for either humans or LLMs. Trying to get the human OR LLM to simply “think better next time” isn’t going to work. You need actual process changes.
This was a rule we always had at my company for any after incident learning reviews: Plan for a world where we are just as stupid tomorrow as we are today. In other words, the action item can’t be “be more careful next time”, because humans forget sometimes (just like LLMs). You will THINK you are being careful, but a detail slips your mind, or you misremember what situation you are in, or you didn’t realize the outside situation changed (e.g. you don’t realize you bumped the keyboard and now you are typing in another console window).
Instead, the safety improvements have to be about guardrails you put up, or mitigations you put in place to prevent disaster the NEXT time you fail to be as careful as you are trying to be.
Because there is always a next time.
Honestly, I think the biggest struggle we are having with LLMs is not knowing when to treat it like a normal computer program and when to treat it like a more human-like intelligence. We run across both issues all the time. We expect it to behave like a human when it doesn’t and then turn around and expect it to behave like a normal computer program when it doesn’t.
This is BRAND NEW territory, and we are going to make so many mistakes while we try to figure it out. We have to expect that if you want to use LLMs for useful things.
Plan for a world where we are just as stupid tomorrow as we are today. In other words, the action item can’t be “be more careful next time”, because humans forget sometimes (just like LLMs).
That’s a great way of putting it, I’ll remember that one (except when I forget...)
I am pretty sure you will remember it during your next learning review… as soon as you get in that learning review, it is suddenly very easy to remember all the things you forgot to do.
Humans don't do this all the time. I think you are conflating things to further this false idea that there is no distance between human thinking and the behavior of LLMs. The kind of rationalization humans sometimes do generally happens over a period of time. Humans are also not "rationalizing" their actions all the time. Also, when humans do what you call "rationalizing," it is to serve some kind of interest, beyond responding to a prompt.
I agree with you a LLM is perfectly capable of explaining its actions.
However it cannot do so after the fact. If there's a reasoning trace it could extract a justification from it. But if there isn't, or if the reasoning trace makes no sense, then the LLM will just lie and make up reasons that sound about right.
Thanks for writing this comment, I think seeing someone’s “first impressions” and then seeing someone else’s response to those thoughts is more interesting and feels more connected socially than just reading a “correct” guide or similar especially when it’s something I’m curious about but wouldn’t necessarily be motivated enough to actually try out myself.
I think it may be that we agree but because the expression “vibe code” has many meanings it’s hard to tell.
I would absolutely still bring a coding agent with me for a project like this, but I would be in the mindset of “I need to understand and be familiar with every line” rather than say, every function signature or every service behaviour.
So it is almost like vibe coding but the abstraction level is lower?
The question I’ve been asking myself recently is, if the act of thinking about the code from scratch is somehow more good than the potential benefit of being able to let that mechanical part be handled by something else, be it another human or an agent.
To be specific I’m referring to a prompt like “next, add a for loop which iterates over the elements in the array and enumerate an index, then call our function $func by reference for each element.” “Is there a more idiomatic way of doing this in $lang?” etc.
This has the advantage to me of letting me code in languages who’s syntax I don’t know or have forgotten, but I’m not sure whether this is trading some sort of short term gain for long term cost yet.
> To be specific I’m referring to a prompt like “next, add a for loop which iterates over the elements in the array and enumerate an index, then call our function $func by reference for each element.”
When you're fluent in a programming language it's quicker to just type that directly in said programming language...
Instead you're training yourself to be able to say this stuff in English which will never be as powerful.
I hear this sentiment a lot but it doesn’t match my experience. I’m an experienced erlang programmer, i’ve shipped thousands of gen-server and gen-statem over the years and coding models can absolutely wire them up faster than I can type them out… I guess if you’re using lombok in Java or something typing @foo probably doesn’t take all that long.
The problem with a lot of these discussions is it's apples and oranges. I'm not experienced with Erlang, but since you say "wiring up" I'm going to assume you're talking about a pretty repetitive task involving putting the right references across several source code files. This is a mechanical process that hasn't annoyed anyone enough yet to write a program to do it. So either Erlang programmers don't care much about their time, or it's a relatively low-frequency thing that doesn't have a big impact overall.
The example I replied to was more the nuts and bolts of programming. It's the thing you're doing 90% of the time. Changes here have a big impact. That's why for almost my entire career I've had expandable "snippets" in my editor to automatically expand, say, "for" to a for loop skeleton where I fill in the variable names. It's like using an electric screwdriver. You don't lose touch with the screws, it just saves you time and effort.
Typing the entire for loop into an LLM in pseudocode actually seems like a regression compared to that. You don't save any typing. But you lose the ability to work independently. You become dependent on a paid subscription and/or powerful hardware just to do what I've been able to do with a keyboard and hardware you can find for free.
It's similar to writing a letter to someone and having it translated to French, but the reader understands English. Why would you do it?
This changes if you go higher level, of course. This is the temptation that LLMs give us. First it's a for loop, then it's an entire class, then entire modules, then it's only a small step to "vibe coding". What we're still figuring out is where this is actually a benefit. Where can we save effort without compromises? I don't think it's typing out code in English, and I don't think it's vibe coding either. Is there something in between? It's too soon to tell.
This fan out approach plus something like Kafka consumer groups is often a better approach to getting workers to take from the same pool anyways, because you can do key based partitioning and therefore have semi stateful consumers (cache, partitioned inserts etc) that are fed similar work.
I think there are two kinds of partition based approach which may cause some confusion if lumped together in this kind of comparison.
Insert and delete with old partition drop vs insert only with old partition drop.
The semantics of the two approaches differ by default but you can achieve the same semantics from either with some higher order changes (partitioning the event space, tracking a cursor per consumer etc).
How does PgQue compare to the insert only partition based approach?
1. partitions are never dropped – they got TRUNCATEd (gracefully) during rotation
2. INSERT-only. Each consumer remembers its position – ID of the last event consumed. This pointer shifts independently for each consumer. It's much closer to Kafka than to task queue systems like ActiveMQ or RabbitMQ.
When you run long-running tx with real XID or read-only in REPEATABLE READ (e.g., pg_dump for long time), or logical slot is unused/lagging, this affects performance badly if you have dead tuples accumulated from DELETEs/UPDATEs, but not promptly vacuumed.
PgQue event tables are append-only, and consumers know how to find next batch of events to consume – so xmin horizon block is not affecting, by design.
Sourcery Show HN: https://news.ycombinator.com/item?id=47996426
The project is currently private, I'd love to have access to its source.